The Skill Library Needs a Bouncer
TL;DR for operators Fleets do not fail only because they forget. They also fail because they remember the wrong thing at the wrong time. That is the practical point of COMAD, a framework for continual offline multi-agent reinforcement learning proposed in Offline Multi-agent Continual Cooperation via Skill Partition and Reuse.1 The paper studies agents that must learn from a stream of offline datasets: first one cooperative task, then another, then another, without interactive trial-and-error and without assuming the required coordination skills stay fixed. That setting is awkward, which is why it is useful. Real deployed systems rarely receive the courtesy of a clean, stationary benchmark and a polite email before the operating conditions change. ...